Dr. J. Thomas Broach Profile

Dr. J. Thomas Broach

Retired at US Army CCDC C5ISR Ctr Night Vision & Electronic Sensors Directorate

SPIE Involvement:

Track Chair | Editor | Author

Proceedings Article | 11 May 2012 Paper

Schedule optimization for IR detection of buried targets

Zenon Derzko, John Eylander, J. Thomas Broach

Proceedings Volume 8357, 83570N (2012) https://doi.org/10.1117/12.918490

KEYWORDS: Meteorology, Atmospheric modeling, Clouds, Solar radiation models, 3D modeling, Target detection, Infrared sensors, Infrared detection, Soil science, Solar radiation

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Proceedings Article | 11 May 2012 Paper

Cloud cover effects on physical soil temperatures with buried targets

Zenon Derzko, Oanh Nguyen, Chung Phan, Richard Lydic, J. Thomas Broach, Timothy Moore

Proceedings Volume 8357, 83570L (2012) https://doi.org/10.1117/12.918468

KEYWORDS: Clouds, Meteorology, Soil science, Solar radiation, Infrared radiation, Detection and tracking algorithms, 3D acquisition, 3D modeling, Finite element methods, Solar radiation models

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Proceedings Article | 11 May 2012 Paper

Rain effects on physical soil temperatures with buried targets

Zenon Derzko, Oanh Nguyen, Chung Phan, Richard Lydic, J. Thomas Broach

Proceedings Volume 8357, 83570M (2012) https://doi.org/10.1117/12.918485

KEYWORDS: Meteorology, Soil science, Clouds, Infrared radiation, Water, Liquids, Solar radiation, 3D acquisition, 3D modeling, Finite element methods

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Proceedings Article | 23 May 2011 Paper

Validating spectral spatial detection based on MMPP formulation

Anh Trang, Sanjeev Agarwal, Thomas Broach, Thomas Smith

Proceedings Volume 8017, 801723 (2011) https://doi.org/10.1117/12.886804

KEYWORDS: Land mines, Target detection, Detection and tracking algorithms, Algorithm development, Mining, Statistical analysis, Device simulation, Expectation maximization algorithms, Detector development, Computer simulations

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Spectral, shape or texture features of the detected targets are used to model the likelihood of the targets to be potential mines in a minefield. However, some potential mines can be false alarms due to the similarity of the mine signatures with natural and other manmade clutter signatures. Therefore, in addition to the target features, spatial distribution of the detected targets can be used to improve the minefield detection performance. In our recently published SPIE paper, we evaluated minefield detection performance for both patterned and unpatterned minefields in highly cluttered environments, simultaneously using both target features and target spatial distributions that define Markov Marked Point Process (MMPP). The results have suggested that proper exploitation of spectral/shape features and spatial distributions can indeed contribute improved performance of patterned and unpatterned minefield detection. Also, the ability of the algorithm to detect the minefields in highly cluttered environments shows the robustness of the developed minefield detection algorithm based on MMPP formulation. Moreover, the results show that the MMPP minefield detection algorithm performs significantly better than the baseline algorithm employing spatial point process with false alarm mitigation. Since these results were based on the simulated data, it is not clear that the MMPP detection algorithm has fully achieved its best performance. To validate its performance, an analytical solution for the minefield detection problem will be developed, and its performance will be compared with the performance of the simulated solution. The analytical solution for the complete minefield detection problem is intractable due to a large number of detections and the variation of the number of detected mines in the minefield process. Therefore, an analytical solution for a simplified detection problem will be derived, and its minefield performance will be compared with the minefield performance obtained from the simulation in the same MMPP framework for different clutter rates.

Proceedings Article | 29 April 2010 Paper

Exploiting spatial distributions for minefield detection in cluttered environment

Anh Trang, Sanjeev Agarwal, Thomas Broach, Thomas Smith

Proceedings Volume 7664, 766426 (2010) https://doi.org/10.1117/12.851488

KEYWORDS: Land mines, Detection and tracking algorithms, Target detection, Algorithm development, Mining, Environmental sensing, Evolutionary algorithms, Sensors, Detector development, Computer simulations

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Proceedings Article | 5 May 2009 Paper

Simultaneously exploiting spectral similarity and spatial distribution for patterned minefield detection

Anh Trang, Sanjeev Agarwal, Thomas Broach, Thomas Smith

Proceedings Volume 7303, 73032B (2009) https://doi.org/10.1117/12.819336

KEYWORDS: Land mines, Target detection, Expectation maximization algorithms, Detection and tracking algorithms, Mining, Sensors, Algorithm development, Fermium, Frequency modulation, Computer simulations

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Proceedings Article | 29 April 2008 Paper

Exploiting "mineness" for scatterable minefield detection

Anh Trang, Sanjeev Agarwal, Thomas Broach, Thomas Smith

Proceedings Volume 6953, 695317 (2008) https://doi.org/10.1117/12.779585

KEYWORDS: Land mines, Target detection, Mining, Fermium, Frequency modulation, Sensors, Expectation maximization algorithms, Detection and tracking algorithms, Algorithm development, Computer simulations

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Proceedings Article | 7 May 2007 Paper

A patterned and un-patterned minefield detection in cluttered environments using Markov marked point process

Anh Trang, Sanjeev Agarwal, Phillip Regalia, Thomas Broach, Thomas Smith

Proceedings Volume 6553, 655313 (2007) https://doi.org/10.1117/12.721368

KEYWORDS: Land mines, Target detection, Mining, Environmental sensing, Sensors, Metals, Detection and tracking algorithms, Data modeling, Data analysis, Image processing

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Proceedings Article | 11 September 2003 Paper

Processing of GPR data from NIITEK landmine detection system

Justin Legarsky, J. Broach, Steven Bishop

Proceedings Volume 5089, (2003) https://doi.org/10.1117/12.497133

KEYWORDS: Land mines, Mining, General packet radio service, Image processing, Signal processing, Filtering (signal processing), Sensors, Image filtering, Electronic filtering, Feature extraction

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Proceedings Article | 22 August 2000 Paper

Mine detection performance by fusing ground-penetrating radars and metal detector

Hanna Haskett, J. Thomas Broach

Proceedings Volume 4038, (2000) https://doi.org/10.1117/12.396311

KEYWORDS: Sensors, General packet radio service, Metals, Land mines, Palladium, Mining, Ground penetrating radar, Data fusion, Gallium, Infrared sensors

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Proceedings Article | 2 August 1999 Paper

Automatic mine detection algorithm using ground penetration radar signatures

Hanna Haskett, J. Thomas Broach

Proceedings Volume 3710, (1999) https://doi.org/10.1117/12.357114

KEYWORDS: Mining, Radar, Land mines, Sensors, Databases, Detection and tracking algorithms, Ground penetrating radar, Algorithm development, Target detection, Fourier transforms

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Proceedings Article | 4 September 1998 Paper

Identification of metallic mines using low-frequency magnetic fields

Lloyd Riggs, Jonathan Mooney, Daniel Lawrence, J. Thomas Broach, Anh Trang

Proceedings Volume 3392, (1998) https://doi.org/10.1117/12.324233

KEYWORDS: Signal to noise ratio, Magnetism, Aluminum, Data modeling, Mining, Monte Carlo methods, Detection and tracking algorithms, Sensors, Error analysis, Metals

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Proceedings Article | 4 September 1998 Paper

Phenomenology of prompt gamma neutron activation analysis in the detection of mines and near-surface ordnance

David Sparrow, Lisa Porter, J. Thomas Broach, Roshni Mehta-Sherbondy

Proceedings Volume 3392, (1998) https://doi.org/10.1117/12.324227

KEYWORDS: Scattering, Nitrogen, Absorption, Explosives, Oxygen, Signal attenuation, Sensors, Silicon, Boron, Land mines

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Prompt gamma neutron activation analysis (PGNAA) has been proposed for confirming the presence of energetic materials as part of a mine or unexploded ordnance detection system. Ancore Corporation (previously SAIC Advanced Nucleonics Division), funded through Night Vision Electro Sciences Directorate by Environmental Security Test Certification Program, has carried out proof-of-concept demonstrations of PGNAA in this confirmatory role at Socorro, NM, and Yuma, AZ. In this, the first part of a two-part paper addressing the use of PGNAA in the detection of surface and near-surface UXO, we explore the phenomenology of PGNAA signals from surface or near-surface ordnance in soil to gain insight into the results of those demonstrations. PGNAA uses the high-energy gamma ray (10.8 MeV) from capture on N¹⁴ as a signature of the presence of nitrogen. This is one of the highest energy gamma rays resulting from neutron capture, and nitrogen is a major constituent of explosives, but a small portion of soil. Thus, PGNAA might be effective at confirming the presence of explosives. The phenomenology of dry soil is dominated by the two most common elements, oxygen and silicon. Neutrons injected into the soil elastically scatter from nuclei (predominantly oxygen), losing energy and propagating in a random walk fashion. Once slowed, neutron capture on soil elements produces a broad gamma-ray spectrum. Capture on Si²⁹ produces a 10.6 MeV gamma, which is not resolvable from the nitrogen signal of interest using scintillation detectors. Thus, PGNAA will need either good resolution detectors, or robust background subtraction to estimate the silicon contribution. For any system unable to resolve the Si²⁹ (10.6 MeV) and N¹⁴(10.8 MeV) gammas there is an inherently low signal to background, resulting primarily from the silicon in the soil. After background subtraction, there remains a challenging signal to noise level, where the noise is partly due to counting statistics and partly due to the modeling of the subtracted background.

Proceedings Article | 4 September 1998 Paper

Assessment of thermal neutron analysis applied to surface and near-surface unexploded ordnance detection

Lisa Porter, David Sparrow, J. Thomas Broach, Roshni Mehta-Sherbondy, Joseph Bendahan

Proceedings Volume 3392, (1998) https://doi.org/10.1117/12.324226

KEYWORDS: Sensors, Explosives, Nitrogen, Scattering, Error analysis, Signal detection, Data modeling, Silicon, Germanium, Detection and tracking algorithms

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Showing 5 of 14 publications

SPIE Conference Volume | 13 June 2013

Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII

SPIE Conference Volume | 10 May 2012

Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVII

SPIE Conference Volume | 23 May 2011

Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI

SPIE Conference Volume | 27 April 2010

Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV

SPIE Conference Volume | 4 May 2009

Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIV

Showing 5 of 16 publications

Conference Committee Involvement (16)

Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII

29 April 2013 | Baltimore, Maryland, United States

Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVII

23 April 2012 | Baltimore, Maryland, United States

Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI

25 April 2011 | Orlando, Florida, United States

Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV

5 April 2010 | Orlando, Florida, United States

Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIV

13 April 2009 | Orlando, Florida, United States

Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII

17 March 2008 | Orlando, Florida, United States

Detection and Remediation Technologies for Mines and Minelike Targets XII

9 April 2007 | Orlando, Florida, United States

Detection and Remediation Technologies for Mines and Minelike Targets XI

17 April 2006 | Orlando (Kissimmee), Florida, United States

Detection and Remediation Technologies for Mines and Minelike Targets X

28 March 2005 | Orlando, Florida, United States

Detection and Remediation Technologies for Mines and Minelike Targets IX

12 April 2004 | Orlando, Florida, United States

Detection and Remediation Technologies for Mines and Minelike Targets VIII

21 April 2003 | Orlando, Florida, United States

Detection and Remediation Technologies for Mines and Minelike Targets VII

1 April 2002 | Orlando, FL, United States

Detection and Remediation Technologies for Mines and Minelike Targets VI

16 April 2001 | Orlando, FL, United States

Detection and Remediation Technologies for Mines and Minelike Targets V

24 April 2000 | Orlando, FL, United States

Detection and Remediation Technologies for Mines and Minelike Targets IV

5 April 1999 | Orlando, FL, United States

Detection and Remediation Technologies for Mines and Minelike Targets III

13 April 1998 | Orlando, FL, United States

Showing 5 of 16 Conference Committees

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